Gene Prediction Chengwei Luo, Amanda McCook, Nadeem Bulsara, Phillip Lee, Neha Gupta, and Divya Anjan Kumar

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1 Gene Prediction Chengwei Luo, Amanda McCook, Nadeem Bulsara, Phillip Lee, Neha Gupta, and Divya Anjan Kumar

2 Gene Prediction Introduction Protein-coding gene prediction RNA gene prediction Modification and finishing Project schema

3 Gene Prediction Introduction Protein-coding gene prediction RNA gene prediction Modification and finishing Project schema

4 Why gene prediction? experimental way?

5 Why gene prediction? Exponential growth of sequences New sequencing technology Metagenomics: ~1% grow in lab

6 How to do it?

7 How to do it? It is a complicated task, let s break it into parts

8 How to do it? It is a complicated task, let s break it into parts Genome

9 How to do it? It is a complicated task, let s break it into parts Genome

10 How to do it? Protein-coding gene prediction Homology Search Phillip Lee & Divya Anjan Kumar ab initio approach Nadeem Bulsara & Neha Gupta

11 How to do it? RNA gene prediction Amanda McCook & Chengwei Luo trna rrna srna

12 Gene Prediction Introduction Protein-coding gene prediction RNA gene prediction Modification and finishing Project schema

13 Homology Search

14 Homology Search

15 Strategy

16 open reading frame(orf)

17 How/Why find ORF?

18 BLAST

19 BLAST

20 BLAST

21 Protein Database Searches

22 SWISSPROT- statistics

23 Pfam-Statistics 11,912 families, with 1,808 new families and 236 families deleted Updated to include metagenomic samples Involves MSA and HMM Only 63%of the Pfam families match the proteins in SWISSPROT and TrEMBL

24 Domain searches

25 Integrating the results 3 possible outcomes: Complete consensus Partial consensus No consensus How do we choose? Scores like E-values Percentage similarity Relevance

26 Limitations of Extrinsic Prediction

27 ab initio Prediction

28 Homology Search is not Enough! Biased and incomplete Database Sequenced genomes are not evenly distributed on the tree of life, and does not reflect the diversity accordingly either. Number of sequenced genomes clustered here

29 ab initio Gene Prediction

30 Features

31 ORFs (6 frames)

32 Codon Statistics

33 Features (Contd.)

34 Probabilistic View

35 Supervised Techniques

36 Unsupervised Techniques

37 Usually Used Tools GeneMark GLIMMER EasyGene PRODIGAL

Used markov chain to represent the statistics of coding and

38 GeneMark Developed in 1993 at Georgia Institute of Technology as the first gene finding tool. Used markov chain to represent the statistics of coding and noncoding reading frames using dicodon statistics. Shortcomings Inability to find exact gene boundaries

39 GeneMark.hmm

40 GeneMark.hmm 9 hidden states were defined Typical gene in the direct strand Typical gene in the reverse strand Atypical gene in the direct strand Atypical gene in the reverse strand Non-coding (intergenic) region Start codons in the direct strand Stop codons in the direct strand Start codons in the reverse strand Stop codons in the reverse strand Probability of any sequence S underlying functional sequence X is calculated as P(X S)=P(x 1,x 2,,x L b 1,b 2,,b L ) Viterbi algorithm then calculates the functional sequence X * such that P(X * S) is the largest among all possible values of X. Ribosome binding site model was also added to augment accuracy in the prediction of translational start sites.

41 GeneMark Even in prokaryotic genomes gene overlaps are quite common RBS feature overcomes this problem by defining a % position nucleotide matrix based on alignment of 325 E coli genes whose RBS signals have already been annotated. Uses a consensus sequence AGGAG to search upstream of any alternative start codons for genes predicted by HMM. GeneMarkS GENEMARKS Considered the best gene prediction tool. Based on unsupervised learning.

42 GLIMMER Maintained by Steven Salzberg, Art Delcher at the University of Maryland, College Park Used IMM (Interpolated Markov Models) for the first time. Predictions based on variable context (oligomers of variable lengths). More flexible than the fixed order Markov models. Principle IMM combines probability based on 0,1..k previous bases, in this case k=8 is used. But this is for oligomers that occur frequently. However, for rarely occurring oligomers, 5th order or lower may also be used.

Glimmer development Glimmer 2 (1999) Increased the sensitivity of prediction by adding concept of ICM (Interpolated Context Model) Glimmer 3 (2007) Overcomes the shortcomings of previous models by

43 Glimmer development Glimmer 2 (1999) Increased the sensitivity of prediction by adding concept of ICM (Interpolated Context Model) Glimmer 3 (2007) Overcomes the shortcomings of previous models by taking in account sum of RBS score, IMM coding potentials and a score for start codons which is dependent on relative frequency of each possible start codon in the same training set used for RBS determination. Algorithm used reverse scoring of IMM by scoring all ORF (open reading frames) in reverse, from the stop codon to start codon. Score being the sum of log likelihood of the bases contained in the ORF.

44 Glimmer3.02

45 PRODIGAL Prokaryotic Dynamic Programming Gene Finding Algorithm Developed at Oak Ridge National Laboratory and the University of Tennessee

46 PRODIGAL-Features

47 PRODIGAL-Features

48 EasyGene Developed at University of Copenhagen Statistical significance is the measure for gene prediction. High quality data set based on similarity in SwissPRot is extracted from genome. Data set used to estimate the HMM where based on ORF score and length statistical significance is calculated. Problem: No standalone version available

49 Comparison of Different Tools

50 Gene Prediction Introduction Protein-coding gene prediction RNA gene prediction Modification and finishing Project schema

51 RNA Gene Prediction

52 Why Predict RNA?

53 Regulatory srna

54 srna Challenges

55 Fundamental Methodology

56 RFAM

57 What Is Covariance? Fig: Christian Weile et al. BMC Genomics (2007) 8:244

58 Noncomparative Prediction Fig: James A. Goodrich & Jennifer F. Kugel, Nature Rev. Mol. Cell Biol. (2006) 7:612

59 Noncomparative Prediction *Rolf Backofen & Wolfgang R. Hess, RNA Biol. (2010) 7:1

60 Comparative+Noncomparative Effective srna prediction in V. cholerae Non-enterobacteria srnapredict2 32 novel srnas predicted 9 tested 6 confirmed Jonathan Livny et al. Nucleic Acids Res. (2005) 33:4096

61 Software *Rolf Backofen & Wolfgang R. Hess, RNA Biol. (2010) 7:1 Eva K. Freyhult et al. Genome Res. (2007) 17:117

62 Gene Prediction Introduction Protein-coding gene prediction RNA gene prediction Modification and finishing Project schema

63 Modification & Finishing Consensus strategy to integrate ab initio results Broken gene recruiting TIS correcting IS calling operon annotating Gene presence/absence analysis

64 Modification & Finishing Consensus strategy Broken gene recruiting pass pass fail candidate fragments homology search ab initio results

65 Modification & Finishing TIS correcting Start codon redundancy:atg, GTG, TTG, CTG Leaderless genes Markov iteration, experimental verified data

66 Modification & Finishing IS calling Operon annotating IS Finder DB

67 Modification & Finishing Gene Presence/absence analysis

68 Gene Prediction Introduction Protein-coding gene prediction RNA gene prediction Modification and finishing Project schema

69 Schema (proposed)

70 Schema (proposed) assembly group

71 Schema (proposed) assembly group

GeneMarkS-2: Raising Standards of Accuracy in Gene Recognition

GeneMarkS-2: Raising Standards of Accuracy in Gene Recognition Alexandre Lomsadze 1^, Shiyuyun Tang 2^, Karl Gemayel 3^ and Mark Borodovsky 1,2,3 ^ joint first authors 1 Wallace H. Coulter Department of